DrugAge Annotations

The compound annotation tables are stored in an SQLite database. This data can be loaded into a user’s R session as follows (here for drugAgeAnnot).

conn <- loadAnnot()
library(RSQLite)
dbListTables(conn)

## [1] "DrugBankAnnot" "cmapAnnot"     "drugAge4"      "drugAgeAnnot" 
## [5] "id_mapping"    "lincs2"        "lincsAnnot"

drugAgeAnnot <- dbReadTable(conn, "drugAgeAnnot")
head(drugAgeAnnot)

##   drugage_id                  compound_name    synonyms                 species
## 1   ida00001                        Vitexin        <NA>  Caenorhabditis elegans
## 2   ida00002                  Cyclosporin A        <NA>  Caenorhabditis elegans
## 3   ida00003                      Histidine L-histidine  Caenorhabditis elegans
## 4   ida00004                        SRT1720        <NA>            Mus musculus
## 5   ida00005 Cordyceps sinensis oral liquid        <NA> Drosophila melanogaster
## 6   ida00006                         Lysine        <NA>  Caenorhabditis elegans
##     strain                dosage avg_lifespan_change max_lifespan_change gender
## 1       N2                 50 µM                   8                 5.3   <NA>
## 2     <NA>                 88 µM                  18                <NA>   <NA>
## 3       N2                  5 mM                  10                <NA>   <NA>
## 4 C57BL/6J 100 mg/kg body weight                 8.8                   0   <NA>
## 5 Oregon-K            0.20 mg/ml                  32                15.4   Male
## 6       N2                  5 mM                   8                <NA>   <NA>
##   significance pubmed_id Comment pref_name     pubchem_cid      DrugBank_id
## 1         <NA>  26535084    <NA>   VITEXIN         5280441             <NA>
## 2         <NA>  24134630    <NA>      <NA>            <NA>          DB00091
## 3         <NA>  25643626    <NA> HISTIDINE   6274, 6971009          DB00117
## 4         <NA>  24582957    <NA>      <NA>            <NA>             <NA>
## 5         <NA>  26239097     Mix      <NA>            <NA>             <NA>
## 6         <NA>  25643626    <NA>    LYSINE 5962, 122198194 DB00123, DB11101

dbDisconnect(conn)

DrugAge SDF

The corresponding structures for the above DrugAge example can be loaded into an SDFset object as follows.

da_sdfset <- loadSDFwithName(source="DrugAge")

Instructions on how to work with SDFset objects are provided in the ChemmineR vignette here. For instance, one can plot any of the loaded structures with the plot function.

ChemmineR::cid(da_sdfset) <- ChemmineR::sdfid(da_sdfset)
ChemmineR::plot(da_sdfset[1])

DrugBank SDF

The SDF from DrugBank can be loaded into R the same way. The corresponding SDF file was downloaded from here. During the import into R ChemmineR checks the validity of the imported compounds.

db_sdfset <- loadSDFwithName(source="DrugBank")

CMAP SDF

The import of the SDF of the CMAP02 database works the same way.

cmap_sdfset <- loadSDFwithName(source="CMAP2")

LINCS SDF

The same applies to the SDF of the small molecules included in the LINCS database.

lincs_sdfset <- loadSDFwithName(source="LINCS")

For reproducibility, the R code for generating the above datasets is included in the inst/scripts/make-data.R file of this package. The file location on a user’s system can be obtained with system.file("scripts/make-data.R", package="customCMPdb").

Load Annotation Database

The SQLite Annotation Database is hosted on Bioconductor’s AnnotationHub. Users can download it to a local AnnotationHub cache directory. The path to this directory can be obtained as follows.

library(AnnotationHub)
ah <- AnnotationHub()
annot_path <- ah[["AH79563"]]

Add Custom Annotation Tables

The following introduces how users can import to the SQLite database their own compound annotation tables. In this case, the corresponding ChEMBL IDs need to be included under the chembl_id column. The name of the custom data set can be specified under the annot_name argument. Note, this name is case insensitive.

chembl_id <- c("CHEMBL1000309", "CHEMBL100014", "CHEMBL10",
               "CHEMBL100", "CHEMBL1000", NA)
annot_tb <- data.frame(cmp_name=paste0("name", 1:6),
        chembl_id=chembl_id,
        feature1=paste0("f", 1:6),
        feature2=rnorm(6))
addCustomAnnot(annot_tb, annot_name="myCustom")

Delete Custom Annotation Tables

The following shows how to delete custom annotation tables by referencing them by their name. To obtain a list of custom annotation tables present in the database, the listAnnot function can be used.

listAnnot()

## [1] "DrugBankAnnot" "cmapAnnot"     "drugAge4"      "drugAgeAnnot" 
## [5] "lincs2"        "lincsAnnot"    "myCustom"

deleteAnnot("myCustom")
listAnnot()

## [1] "DrugBankAnnot" "cmapAnnot"     "drugAge4"      "drugAgeAnnot" 
## [5] "lincs2"        "lincsAnnot"

Set to Default

The defaultAnnot function sets the annotation SQLite database back to the original version provided by customCMPdb. This is achieved by deleting the existing (e.g. custom) database and re-downloading a fresh instance from AnnotationHub.

defaultAnnot()

Description of Annotation Tables in SQLite Database

The DrugAge database is manually curated by experts. It contains an extensive compilation of drugs, compounds and supplements (including natural products and nutraceuticals) with anti-aging properties that extend longevity in model organisms (Barardo et al. 2017). The DrugAge build2 database was downloaded from here as a CSV file. The downloaded drugage.csv file contains compound_name, synonyms, species, strain, dosage, avg_lifespan_change, max_lifespan_change, gender, significance, and pubmed_id annotation columns. Since the DrugAge database only contains the drug name as identifiers, it is necessary to map the drug name to other uniform drug identifiers, such as ChEMBL IDs. In this package, the drug names have been mapped to ChEMBL (Gaulton et al. 2012), PubChem (Kim et al. 2019) and DrugBank IDs semi-manually and stored under the inst/extdata directory named as drugage_id_mapping.tsv. Part of the id mappings in the drugage_id_mapping.tsv table is generated by the function for compound names that have ChEMBL ids from the ChEMBL database (version 24). The missing IDs were added manually. A semi-manual approach was to use this web service. After the semi-manual process, the left ones were manually mapped to ChEMBL, PubChem and DrugBank ids. The entries that are mixture like green tee extract or peptide like Bacitracin were commented. Then the drugage_id_mapping table was built into the annotation SQLite database named as compoundCollection_0.1.db with table name of drugAgeAnnot by buildDrugAgeDB function.

The DrugBank annotation table (DrugBankAnnot) was downloaded from the DrugBank database in xml file. The most recent release version at the time of writing this document is 5.1.5.
The extracted xml file was processed by the function in this package. dbxml2df and df2SQLite functions in this package were used to load the xml file into R and covert to a data.frame R object, then stored in the compoundCollection SQLite annotation database. There are 55 annotation columns in the DrugBank annotation table, such as drugbank_id, name, description, cas-number, groups, indication, pharmacodynamics, mechanism-of-action, toxicity, metabolism, half-life, protein-binding, classification, synonyms, international-brands, packagers, manufacturers, prices, dosages, atc-codes, fda-label, pathways, targets. The DrugBank id to ChEMBL id mappings were obtained from UniChem.

The CMAP02 annotation table (cmapAnnot) was processed from the downloaded compound instance table using the buildCMAPdb function defined by this package. The CMAP02 instance table contains the following drug annotation columns: instance_id, batch_id, cmap_name, INN1, concentration (M), duration (h), cell2, array3, perturbation_scan_id, vehicle_scan_id4, scanner, vehicle, vendor, catalog_number, catalog_name. Drug names are used as drug identifies. The buildCMAPdb function maps the drug names to external drug ids including UniProt (The UniProt Consortium 2017), PubChem, DrugBank and ChemBank (Seiler et al. 2008) ids. It also adds additional annotation columns such as directionality, ATC codes and SMILES structure. The generated cmap.db SQLite database from buildCMAPdb function contains both compound annotation table and structure information. The ChEMBL id mappings were further added to the annotation table via PubChem CID to ChEMBL id mappings from UniChem. The CMAP02 annotation table was stored in the compoundCollection SQLite annotation database. Then the CMAP internal IDs to ChEMBL id mappings were added to the ID mapping table.

The LINCS 2017 compound annotation table (lincsAnnot) was downloaded from GEO where only compounds were selected. The annotation columns are lincs_id, pert_name, pert_type, is_touchstone, inchi_key_prefix, inchi_key, canonical_smiles, pubchem_cid. The annotation table was stored in the compoundCollection SQLite annotation database. Since the annotation only contains LINCS id to PubChem CID mapping, the LINCS ids were also mapped to ChEMBL ids via inchi key.

The SQLite annotation database also contains compound annotation tables of drugAge4 and lincs2. They contain annotations for compounds in DrugAge build 4 database, and the newest LINCS beta database released in 2020 at CLUE.